ABSTRACTA cup of coffee is the final product of a complex chain of operations. Wet postharvest processing of coffee is one of these operations, which involves a fermentation that inevitably has to be performed on-farm. During wet coffee processing, the interplay between microbial activities and endogenous bean metabolism results in a specific flavor precursor profile of the green coffee beans. Yet, how specific microbial communities and the changing chemical compositions of the beans determine the flavor of a cup of coffee remains underappreciated. Through a multiphasic approach, the establishment of the microbial communities, as well as their prevalence during wet processing ofCoffea arabica, was followed at an experimental farm in Ecuador. Also, the metabolites produced by the microorganisms and those of the coffee bean metabolism were monitored to determine their influence on the green coffee bean metabolite profile over time. The results indicated that lactic acid bacteria were prevalent well before the onset of fermentation and that the fermentation duration entailed shifts in their communities. The fermentation duration also affected the compositions of the beans, so that longer-fermented coffee had more notes that are preferred by consumers. As a consequence, researchers and coffee growers should be aware that the flavor of a cup of coffee is determined before as well as during on-farm processing and that under the right conditions, longer fermentation times can be favorable, although the opposite is often believed.IMPORTANCECoffee needs to undergo a long chain of events to transform from coffee cherries to a beverage. The coffee postharvest processing is one of the key phases that convert the freshly harvested cherries into green coffee beans before roasting and brewing. Among multiple existing processing methods, the wet processing has been usually applied for Arabica coffee and produces decent quality of both green coffee beans and the cup of coffee. In the present case study, wet processing was followed by a multiphasic approach through both microbiological and metabolomic analyses. The impacts of each processing step, especially the fermentation duration, were studied in detail. Distinct changes in microbial ecosystems, processing waters, coffee beans, and sensory quality of the brews were found. Thus, through fine-tuning of the parameters in each step, the microbial diversity and endogenous bean metabolism can be altered during coffee postharvest processing and hence provide potential to improve coffee quality.
Application of colour, shape, and texture parameters for classifying the defect of Gayo Arabica green coffee bean using computer vision